发表机构
Charles University（查理大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究机器学习在不规则及各向异性自适应网格上计算SUPG稳定化参数的方法，通过数值实验验证并扩展其适用性。
AI 中文摘要
使用稳定化方法求解对流占优问题通常需要指定稳定化参数，这些参数的最优选择是未知的，但会显著影响近似解的质量。在作者最近的工作中，提出了一种通过机器学习计算这些参数的方法，并将其应用于对流扩散方程的流线迎风/Petrov-Galerkin（SUPG）方法。本文针对不规则网格对该方法进行了数值研究，并将其扩展到通过各向异性网格自适应获得的网格上。
英文摘要
Solution of convection-dominated problems using stabilized methods often requires to specify stabilization parameters whose optimal choice is not known but which considerably influence the quality of the approximate solution. In a recent work of the authors, an approach for computing these parameters by machine learning was proposed and applied to the streamline upwind/Petrov-Galerkin (SUPG) method for convection-diffusion equations. In the present paper, this approach is studied numerically for unstructured meshes and extended to meshes obtained by anisotropic mesh adaptation.
